---
license: mit
tags:
- biology
- chemistry
- molecular-property-prediction
- gnn
- drug-discovery
---

# molecular_bioactivity_predictor_gnn

## Overview
This model utilizes a Graph Isomorphism Network (GIN) to predict the bioactivity and binding affinity ($K_i$) of small molecules against specific protein targets. By representing molecules as graphs where atoms are nodes and bonds are edges, the model captures complex spatial relationships crucial for pharmacological efficacy.



## Model Architecture
The model implements a **Message Passing Neural Network (MPNN)** using the GIN convolution operator.
- **Node Features**: Includes atomic number, chirality, hybridization, and formal charge.
- **Edge Features**: Includes bond type (single, double, triple, aromatic) and stereochemistry.
- **Readout Layer**: Global Mean Pooling followed by a 3-layer MLP.
- **Aggregation**: The update rule for node $i$ at layer $k$ is defined as:
$$h_i^{(k)} = \text{MLP}^{(k)} \left( (1 + \epsilon^{(k)}) \cdot h_i^{(k-1)} + \sum_{j \in \mathcal{N}(i)} h_j^{(k-1)} \right)$$

## Intended Use
- **Virtual Screening**: Ranking massive libraries of compounds to identify potential lead candidates for synthesis.
- **ADMET Prediction**: Estimating the solubility and lipophilicity of new chemical entities.
- **Target Profiling**: Predicting potential off-target interactions to minimize clinical side effects.

## Limitations
- **Stereoisomers**: The model may struggle to differentiate between complex enantiomers that have identical connectivity but different biological activity.
- **Large Molecules**: It is primarily validated on small molecules (MW < 800 Da) and may not generalize to biologics or large macrocycles.
- **Dataset Bias**: Prediction accuracy is highly dependent on the chemical diversity of the training set (e.g., ChEMBL or PDBBind).